USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers
89% of respondents would recommend this to a friend
€ 129
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Coral USB Accelerator provides high performance ML inferencing with a low power cost over a USB 3.0 interface.
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What Stands Out
Product Details
- Featuring the Edge TPU, designed and built by Google
- Provides high performance ML inferencing with low power cost over a USB 3.0 interface
- Executes state-of-the-art mobile vision models at over 100 fps in a power-efficient manner
- Allows fast ML inferencing to embedded AI devices in a power-efficient and privacy-preserving way
- Models developed in TensorFlow Lite and then compiled to run on the USB Accelerator
- Key benefits: High speed TensorFlow Lite inferencing, low power, small footprint
| Package Weight | 1.0000 Pound |
Who Should Buy?
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AI Enthusiasts
Great for hobbyists experimenting with AI projects on Raspberry Pi or other embedded systems requiring on-device machine learning.
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Developers
Ideal for software developers looking to prototype and deploy machine learning applications in a compact, efficient environment.
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Robotics Engineers
Designed for robotics projects needing efficient computation power for image processing and real-time decision-making capabilities.
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Casual Users
Not suitable for individuals seeking simple computing solutions without advanced machine learning applications or expertise.
Product Description
USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers
About This Item
Introducing the Google Coral USB Edge TPU ML Accelerator - the ultimate coprocessor for Raspberry Pi and other embedded single board computers. This powerful device brings advanced machine learning (ML) inferencing capabilities to your existing Linux systems. Featuring the highly efficient Edge TPU, a small ASIC designed and developed by Google, the Coral USB Accelerator provides you with high-performance ML inferencing while consuming minimal power through a USB 3.0 interface. With its cutting-edge technology, this accelerator can execute state-of-the-art mobile vision models, such as MobileNet v2, at over 100 frames per second in a power-efficient manner. The Coral USB Accelerator allows you to enable fast ML inferencing on your embedded AI devices, all while maintaining a power-efficient and privacy-preserving approach.
Models are developed using TensorFlow Lite and then compiled to run seamlessly on this accelerator, providing you with high-speed inferencing capabilities. One of the key benefits of the Edge TPU is its ability to deliver low-power ML inferencing without compromising on performance. This coprocessor is equipped with an Arm 32-bit Cortex-M0+ Microprocessor (MCU) with up to 32 MHz clock speed, ensuring outstanding speed and efficiency. In addition to its impressive performance, the Coral USB Accelerator also boasts a small footprint, making it a flexible and versatile solution for your embedded systems. It comes with a USB 3.1 (gen 1) port and cable, ensuring a SuperSpeed data transfer rate of up to 5Gb/s. The Coral USB Accelerator is fully compatible with Google Cloud and supports Debian Linux on host CPUs.
You can develop models using TensorFlow and take advantage of its compatibility with popular architectures like MobileNet and Inception. Furthermore, the device supports custom architectures, opening up endless possibilities for your ML projects. At Ubuy, we offer a range of e-commerce options for Raspberry Pi and other embedded single board computers, allowing you to conveniently shop for high-quality embedded computing products and components. Whether you are an AI enthusiast, a hobbyist, or a professional developer, our online store provides you with a seamless shopping experience for all your embedded system needs. Discover the future of embedded AI with the Google Coral USB Edge TPU ML Accelerator.
Shop now and unlock the potential of your embedded systems!.
Product Buying Guide
The Google Coral USB Edge TPU ML Accelerator coprocessor is designed to bring powerful machine learning (ML) inferencing capabilities to existing Linux systems. It features the Edge TPU, a custom-made ASIC by Google, and provides high-performance ML inferencing with low power consumption over a USB 3.0 interface. This guide will help you explore the product's specifications, key features, usage scenarios, competitor comparison, user reviews, price analysis, and essential buying considerations to help you make an informed decision.
Product Specifications
- Arm 32-bit Cortex-M0+ microprocessor (MCU): Up to 32 MHz max
- 16 KB flash memory with ECC
- 2 KB RAM
- Connections: USB 3.1 (gen 1) port and cable (SuperSpeed, 5Gb/s transfer speed)
Key Features
- Google Edge TPU ML accelerator coprocessor
- USB 3.0 Type-C socket
- Supports Debian Linux on host CPU
- Models are built using TensorFlow
- Fully supports MobileNet and Inception architectures
Usage Scenarios
- High-speed TensorFlow Lite inferencing
- Low power consumption
- Small footprint, ideal for embedded AI devices
Usage Scenarios
The Google Coral USB Edge TPU ML Accelerator coprocessor competes with similar products available in the market. These competitors offer alternative ML acceleration solutions with varying features and performance.
Some User Review
- The speed and low power consumption of the Edge TPU accelerator have significantly improved my ML inferencing tasks.
- The compatibility with Debian Linux and TensorFlow makes it a versatile and efficient ML inferencing solution.
Competitors
The Google Coral USB Edge TPU ML Accelerator coprocessor offers competitive pricing in the market, considering its cutting-edge ML inferencing capabilities, power efficiency, and Google's reliability. It provides excellent value for users seeking high-performance ML acceleration.
Buying Considerations
- Ensure compatibility with your specific embedded system or single-board computer to maximize the benefits of the Edge TPU accelerator.
- Evaluate the ML inferencing requirements of your projects to determine if the features and performance of the Edge TPU align with your needs.
Conclusion
With its powerful machine learning inferencing capabilities, low power consumption, and diverse usage scenarios, the Google Coral USB Edge TPU ML Accelerator coprocessor offers a compelling solution for embedded AI devices and ML projects. It's a versatile, reliable, and cost-effective choice for enhancing ML performance on existing Linux systems.
View LessThe Google Coral USB Edge TPU ML Accelerator coprocessor is designed to bring powerful machine learning (ML) inferencing capabilities to existing Linux systems. It features the Edge TPU, a custom-made ASIC by Google, and provides high-performance ML inferencing with low power consumption over a USB 3.0 interface. This guide will help you explore the product's specifications, key features, usage scenarios, competitor comparison, user reviews, price analysis, and essential buying considerations to help you make an informed decision. Continue Reading
Customer Questions & Answers
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Google Coral Motherboards Editorial Review
USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers is a compact and efficient solution for enhancing AI capabilities in your projects. Users have reported significant reductions in CPU usage, vital for running multiple cameras with Frigate and utilizing the device as part of their self-hosted Home Assistant setups. The convenience of plug-and-play functionality and straightforward configuration stands out, enabling quick integration into existing systems. However, potential buyers should be aware that it may run hot during use, a common aspect of its operation. Overall, this device excels in providing support for real-time object detection tasks while easing strain on the host CPU.
Customer Reviews & Ratings
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Pros
- Reduces CPU load effectively during multi-camera use
- Easy to integrate with Home Assistant and Frigate
- Compact design suitable for various setups
- Provides decent accuracy for real-time detection
- Plug-and-play setup with minimal configuration
Cons
- May run hot during operation; normal for this device
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€ 129
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Features & Benefits
- Brings powerful ML inferencing capabilities to existing Linux systems
- Execute state-of-the-art mobile vision models in a power-efficient manner
- Great for fast ML inferencing to embedded AI devices in a privacy-preserving way
- Fully supports MobileNet and Inception architectures though custom architectures are possible
- Compatible with Google Cloud
- Features Google Edge TPU ML accelerator coprocessor, USB 3.0 Type-C socket, Debian Linux on host CPU and models built with TensorFlow
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